@langchain/core/dist/language_models/base.d.cts.map

Core LangChain.js abstractions and schemas

{"version":3,"file":"base.d.cts","names":["TiktokenModel","ZodType","ZodTypeV3","$ZodType","ZodTypeV4","BaseCache","BasePromptValueInterface","BaseMessage","BaseMessageLike","MessageContent","LLMResult","CallbackManager","Callbacks","AsyncCaller","AsyncCallerParams","Runnable","RunnableInterface","RunnableConfig","JSONSchema","InferInteropZodOutput","InteropZodObject","InteropZodType","ModelProfile","getModelNameForTiktoken","getEmbeddingContextSize","getModelContextSize","isOpenAITool","ToolDefinition","CalculateMaxTokenProps","calculateMaxTokens","prompt","modelName","Promise","SerializedLLM","Record","BaseLangChainParams","BaseLangChain","RunInput","RunOutput","CallOptions","BaseLanguageModelParams","BaseLanguageModelTracingCallOptions","BaseLanguageModelCallOptions","FunctionDefinition","FunctionCallOption","BaseFunctionCallOptions","BaseLanguageModelInput","StructuredOutputType","StructuredOutputMethodOptions","IncludeRaw","StructuredOutputMethodParams","BaseLanguageModelInterface","LanguageModelOutput","LanguageModelLike","BaseLanguageModel","callbacks","callbackManager","config","TokenUsage"],"sources":["../../src/language_models/base.d.ts"],"sourcesContent":["import type { TiktokenModel } from \"js-tiktoken/lite\";\nimport type { ZodType as ZodTypeV3 } from \"zod/v3\";\nimport type { $ZodType as ZodTypeV4 } from \"zod/v4/core\";\nimport { type BaseCache } from \"../caches/index.js\";\nimport { type BasePromptValueInterface } from \"../prompt_values.js\";\nimport { type BaseMessage, type BaseMessageLike, type MessageContent } from \"../messages/base.js\";\nimport { type LLMResult } from \"../outputs.js\";\nimport { CallbackManager, Callbacks } from \"../callbacks/manager.js\";\nimport { AsyncCaller, AsyncCallerParams } from \"../utils/async_caller.js\";\nimport { Runnable, type RunnableInterface } from \"../runnables/base.js\";\nimport { RunnableConfig } from \"../runnables/config.js\";\nimport { JSONSchema } from \"../utils/json_schema.js\";\nimport { InferInteropZodOutput, InteropZodObject, InteropZodType } from \"../utils/types/zod.js\";\nimport { ModelProfile } from \"./profile.js\";\nexport declare const getModelNameForTiktoken: (modelName: string) => TiktokenModel;\nexport declare const getEmbeddingContextSize: (modelName?: string | undefined) => number;\n/**\n * Get the context window size (max input tokens) for a given model.\n *\n * Context window sizes are sourced from official model documentation:\n * - OpenAI: https://platform.openai.com/docs/models\n * - Anthropic: https://docs.anthropic.com/claude/docs/models-overview\n * - Google: https://ai.google.dev/gemini/docs/models/gemini\n *\n * @param modelName - The name of the model\n * @returns The context window size in tokens\n */\nexport declare const getModelContextSize: (modelName: string) => number;\n/**\n * Whether or not the input matches the OpenAI tool definition.\n * @param {unknown} tool The input to check.\n * @returns {boolean} Whether the input is an OpenAI tool definition.\n */\nexport declare function isOpenAITool(tool: unknown): tool is ToolDefinition;\ninterface CalculateMaxTokenProps {\n    prompt: string;\n    modelName: TiktokenModel;\n}\nexport declare const calculateMaxTokens: ({ prompt, modelName, }: CalculateMaxTokenProps) => Promise<number>;\nexport type SerializedLLM = {\n    _model: string;\n    _type: string;\n} & Record<string, any>;\nexport interface BaseLangChainParams {\n    verbose?: boolean;\n    callbacks?: Callbacks;\n    tags?: string[];\n    metadata?: Record<string, unknown>;\n}\n/**\n * Base class for language models, chains, tools.\n */\nexport declare abstract class BaseLangChain<RunInput, RunOutput, CallOptions extends RunnableConfig = RunnableConfig> extends Runnable<RunInput, RunOutput, CallOptions> implements BaseLangChainParams {\n    /**\n     * Whether to print out response text.\n     */\n    verbose: boolean;\n    callbacks?: Callbacks;\n    tags?: string[];\n    metadata?: Record<string, unknown>;\n    get lc_attributes(): {\n        [key: string]: undefined;\n    } | undefined;\n    constructor(params: BaseLangChainParams);\n}\n/**\n * Base interface for language model parameters.\n * A subclass of {@link BaseLanguageModel} should have a constructor that\n * takes in a parameter that extends this interface.\n */\nexport interface BaseLanguageModelParams extends AsyncCallerParams, BaseLangChainParams {\n    /**\n     * @deprecated Use `callbacks` instead\n     */\n    callbackManager?: CallbackManager;\n    cache?: BaseCache | boolean;\n}\nexport interface BaseLanguageModelTracingCallOptions {\n    /**\n     * Describes the format of structured outputs.\n     * This should be provided if an output is considered to be structured\n     */\n    ls_structured_output_format?: {\n        /**\n         * An object containing the method used for structured output (e.g., \"jsonMode\").\n         */\n        kwargs: {\n            method: string;\n        };\n        /**\n         * The JSON schema describing the expected output structure.\n         */\n        schema?: JSONSchema;\n    };\n}\nexport interface BaseLanguageModelCallOptions extends RunnableConfig, BaseLanguageModelTracingCallOptions {\n    /**\n     * Stop tokens to use for this call.\n     * If not provided, the default stop tokens for the model will be used.\n     */\n    stop?: string[];\n}\nexport interface FunctionDefinition {\n    /**\n     * The name of the function to be called. Must be a-z, A-Z, 0-9, or contain\n     * underscores and dashes, with a maximum length of 64.\n     */\n    name: string;\n    /**\n     * The parameters the functions accepts, described as a JSON Schema object. See the\n     * [guide](https://platform.openai.com/docs/guides/gpt/function-calling) for\n     * examples, and the\n     * [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for\n     * documentation about the format.\n     *\n     * To describe a function that accepts no parameters, provide the value\n     * `{\"type\": \"object\", \"properties\": {}}`.\n     */\n    parameters: Record<string, unknown> | JSONSchema;\n    /**\n     * A description of what the function does, used by the model to choose when and\n     * how to call the function.\n     */\n    description?: string;\n}\nexport interface ToolDefinition {\n    type: \"function\";\n    function: FunctionDefinition;\n}\nexport type FunctionCallOption = {\n    name: string;\n};\nexport interface BaseFunctionCallOptions extends BaseLanguageModelCallOptions {\n    function_call?: FunctionCallOption;\n    functions?: FunctionDefinition[];\n}\nexport type BaseLanguageModelInput = BasePromptValueInterface | string | BaseMessageLike[];\nexport type StructuredOutputType = InferInteropZodOutput<InteropZodObject>;\nexport type StructuredOutputMethodOptions<IncludeRaw extends boolean = false> = {\n    name?: string;\n    method?: \"functionCalling\" | \"jsonMode\" | \"jsonSchema\" | string;\n    includeRaw?: IncludeRaw;\n    /** Whether to use strict mode. Currently only supported by OpenAI models. */\n    strict?: boolean;\n};\n/** @deprecated Use StructuredOutputMethodOptions instead */\nexport type StructuredOutputMethodParams<RunOutput, IncludeRaw extends boolean = false> = {\n    /** @deprecated Pass schema in as the first argument */\n    schema: InteropZodType<RunOutput> | Record<string, any>;\n    name?: string;\n    method?: \"functionCalling\" | \"jsonMode\";\n    includeRaw?: IncludeRaw;\n};\nexport interface BaseLanguageModelInterface<RunOutput = any, CallOptions extends BaseLanguageModelCallOptions = BaseLanguageModelCallOptions> extends RunnableInterface<BaseLanguageModelInput, RunOutput, CallOptions> {\n    get callKeys(): string[];\n    generatePrompt(promptValues: BasePromptValueInterface[], options?: string[] | CallOptions, callbacks?: Callbacks): Promise<LLMResult>;\n    _modelType(): string;\n    _llmType(): string;\n    getNumTokens(content: MessageContent): Promise<number>;\n    /**\n     * Get the identifying parameters of the LLM.\n     */\n    _identifyingParams(): Record<string, any>;\n    serialize(): SerializedLLM;\n}\nexport type LanguageModelOutput = BaseMessage | string;\nexport type LanguageModelLike = Runnable<BaseLanguageModelInput, LanguageModelOutput>;\n/**\n * Base class for language models.\n */\nexport declare abstract class BaseLanguageModel<RunOutput = any, CallOptions extends BaseLanguageModelCallOptions = BaseLanguageModelCallOptions> extends BaseLangChain<BaseLanguageModelInput, RunOutput, CallOptions> implements BaseLanguageModelParams, BaseLanguageModelInterface<RunOutput, CallOptions> {\n    /**\n     * Keys that the language model accepts as call options.\n     */\n    get callKeys(): string[];\n    /**\n     * The async caller should be used by subclasses to make any async calls,\n     * which will thus benefit from the concurrency and retry logic.\n     */\n    caller: AsyncCaller;\n    cache?: BaseCache;\n    constructor({ callbacks, callbackManager, ...params }: BaseLanguageModelParams);\n    abstract generatePrompt(promptValues: BasePromptValueInterface[], options?: string[] | CallOptions, callbacks?: Callbacks): Promise<LLMResult>;\n    abstract _modelType(): string;\n    abstract _llmType(): string;\n    private _encoding?;\n    /**\n     * Get the number of tokens in the content.\n     * @param content The content to get the number of tokens for.\n     * @returns The number of tokens in the content.\n     */\n    getNumTokens(content: MessageContent): Promise<number>;\n    protected static _convertInputToPromptValue(input: BaseLanguageModelInput): BasePromptValueInterface;\n    /**\n     * Get the identifying parameters of the LLM.\n     */\n    _identifyingParams(): Record<string, any>;\n    /**\n     * Create a unique cache key for a specific call to a specific language model.\n     * @param callOptions Call options for the model\n     * @returns A unique cache key.\n     */\n    _getSerializedCacheKeyParametersForCall({ config, ...callOptions }: CallOptions & {\n        config?: RunnableConfig;\n    }): string;\n    /**\n     * @deprecated\n     * Return a json-like object representing this LLM.\n     */\n    serialize(): SerializedLLM;\n    /**\n     * @deprecated\n     * Load an LLM from a json-like object describing it.\n     */\n    static deserialize(_data: SerializedLLM): Promise<BaseLanguageModel>;\n    /**\n     * Return profiling information for the model.\n     *\n     * @returns {ModelProfile} An object describing the model's capabilities and constraints\n     */\n    get profile(): ModelProfile;\n    withStructuredOutput?<RunOutput extends Record<string, any> = Record<string, any>>(schema: ZodTypeV3<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<false>): Runnable<BaseLanguageModelInput, RunOutput>;\n    withStructuredOutput?<RunOutput extends Record<string, any> = Record<string, any>>(schema: ZodTypeV3<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<true>): Runnable<BaseLanguageModelInput, {\n        raw: BaseMessage;\n        parsed: RunOutput;\n    }>;\n    withStructuredOutput?<RunOutput extends Record<string, any> = Record<string, any>>(schema: ZodTypeV4<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<false>): Runnable<BaseLanguageModelInput, RunOutput>;\n    withStructuredOutput?<RunOutput extends Record<string, any> = Record<string, any>>(schema: ZodTypeV4<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<true>): Runnable<BaseLanguageModelInput, {\n        raw: BaseMessage;\n        parsed: RunOutput;\n    }>;\n    /**\n     * Model wrapper that returns outputs formatted to match the given schema.\n     *\n     * @template {BaseLanguageModelInput} RunInput The input type for the Runnable, expected to be the same input for the LLM.\n     * @template {Record<string, any>} RunOutput The output type for the Runnable, expected to be a Zod schema object for structured output validation.\n     *\n     * @param {InteropZodType<RunOutput>} schema The schema for the structured output. Either as a Zod schema or a valid JSON schema object.\n     *   If a Zod schema is passed, the returned attributes will be validated, whereas with JSON schema they will not be.\n     * @param {string} name The name of the function to call.\n     * @param {\"functionCalling\" | \"jsonMode\"} [method=functionCalling] The method to use for getting the structured output. Defaults to \"functionCalling\".\n     * @param {boolean | undefined} [includeRaw=false] Whether to include the raw output in the result. Defaults to false.\n     * @returns {Runnable<RunInput, RunOutput> | Runnable<RunInput, { raw: BaseMessage; parsed: RunOutput }>} A new runnable that calls the LLM with structured output.\n     */\n    withStructuredOutput?<RunOutput extends Record<string, any> = Record<string, any>>(schema: InteropZodType<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<boolean>): Runnable<BaseLanguageModelInput, RunOutput> | Runnable<BaseLanguageModelInput, {\n        raw: BaseMessage;\n        parsed: RunOutput;\n    }>;\n}\n/**\n * Shared interface for token usage\n * return type from LLM calls.\n */\nexport interface TokenUsage {\n    completionTokens?: number;\n    promptTokens?: number;\n    totalTokens?: number;\n}\nexport {};\n//# 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